[Docs] Adds a migration guide SQL compatible null handling (#16704)

Co-authored-by: Clint Wylie <cjwylie@gmail.com>
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>

docs/release-info/migr-ansi-sql-null.md

@@ -0,0 +1,386 @@
+---
+id: migr-ansi-sql-null
+title: "Migration guide: SQL compliant mode"
+sidebar_label: SQL compliant mode
+---
+
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+import TabItem from '@theme/TabItem';
+
+In Apache Druid 28.0.0, the default [null handling](../querying/sql-data-types.md#null-values) mode changed to be compliant with the ANSI SQL standard.
+This guide provides strategies for Druid operators who rely on legacy Druid null handling behavior in their applications to transition to SQL compliant mode.
+Legacy mode is planned for removal from Druid. 
+
+## SQL compliant null handling
+
+As of Druid 28.0.0, Druid writes segments in an ANSI SQL compatible null handling mode by default.
+This means that Druid stores null values distinctly from empty strings for string dimensions and distinctly from 0 for numeric dimensions.
+
+This can impact your application behavior because the ANSI SQL standard defines any comparison to null to be unknown.
+According to this three-valued logic, `x <> 'some value'` only returns non-null values.
+
+The default Druid configurations for 28.0.0 and later that enable ANSI SQL compatible null handling mode are the following:
+
+* `druid.generic.useDefaultValueForNull=false`
+* `druid.expressions.useStrictBooleans=true`
+* `druid.generic.useThreeValueLogicForNativeFilters=true` 
+
+Follow the [Null handling tutorial](../tutorials/tutorial-sql-null.md) to learn how the default null handling works in Druid.
+
+## Legacy null handling and two-value logic
+
+Prior to Druid 28.0.0, Druid defaulted to a legacy mode which stored default values instead of nulls.
+In legacy mode, Druid created segments with the following characteristics at ingestion time:
+
+- String columns couldn't distinguish an empty string, '', from null.
+    Therefore, Druid treated them both as interchangeable values.
+- Numeric columns couldn't represent null valued rows.
+    Therefore Druid stored 0 instead of null. 
+
+The Druid configurations for the deprecated legacy mode were the following:
+
+* `druid.generic.useDefaultValueForNull=true`
+* `druid.expressions.useStrictBooleans=false`
+* `druid.generic.useThreeValueLogicForNativeFilters=true`
+
+These configurations are deprecated and scheduled for removal.
+After the configurations are removed, Druid will ignore them if they exist in your configuration files and use the default SQL compliant mode.
+
+## Migrate to SQL compliant mode
+
+If your business logic relies on the behavior of legacy mode, you have the following options to operate Druid in an ANSI SQL compatible null handling mode:
+
+- Modify incoming data to either [avoid nulls](#replace-null-values-at-ingestion-time) or [avoid empty strings](#coerce-empty-strings-to-null-at-ingestion-time) to achieve the same query behavior as legacy mode. This means modifying your ingestion SQL queries and ingestion specs to handle nulls or empty strings.
+    For example, replacing a null for a string column with an empty string or a 0 for a numeric column.
+    However, it means that your existing queries should operate as if Druid were in legacy mode.
+    If you do not care about preserving null values, this is a good option for you.
+
+- Preserve null values and [update all of your SQL queries to be ANSI SQL compliant](#rewrite-your-queries-to-be-sql-compliant).
+    This means you can preserve the incoming data with nulls intact.
+    However, you must rewrite any affected client-side queries to be ANSI SQL compliant.
+    If you have a requirement to preserve null values, choose this option.
+
+### Replace null values at ingestion time
+
+If you don't need to preserve null values within Druid, you can use a transform at ingestion time to replace nulls with other values.
+
+Consider the following input data:
+
+```json
+{"time":"2024-01-01T00:00:00.000Z","string_example":"my_string","number_example":99}
+{"time":"2024-01-02T00:00:00.000Z","string_example":"","number_example":0}
+{"time":"2024-01-03T00:00:00.000Z","string_example":null,"number_example":null}
+```
+ 
+The following example illustrates how to use COALESCE and NVL at ingestion time to avoid null values in Druid:
+
+<Tabs>
+
+<TabItem value="0" label="SQL-based batch">
+
+```sql
+REPLACE INTO "no_nulls_example" OVERWRITE ALL
+WITH "ext" AS (
+  SELECT *
+  FROM TABLE(
+    EXTERN(
+      '{"type":"inline","data":"{\"time\":\"2024-01-01T00:00:00.000Z\",\"string_example\":\"my_string\",\"number_example\":99}\n{\"time\":\"2024-01-02T00:00:00.000Z\",\"string_example\":\"\",\"number_example\":0}\n{\"time\":\"2024-01-03T00:00:00.000Z\",\"string_example\":null,\"number_example\":null}"}',
+      '{"type":"json"}'
+    )
+  ) EXTEND ("time" VARCHAR, "string_example" VARCHAR, "number_example" BIGINT)
+)
+SELECT
+  TIME_PARSE("time") AS "__time",
+  -- Replace any null string values with an empty string
+  COALESCE("string_example",'') AS string_example,
+  -- Replace any null numeric values with 0
+  NVL("number_example",0) AS number_example
+FROM "ext"
+PARTITIONED BY MONTH
+```
+</TabItem>
+
+<TabItem value="1" label="JSON-based batch">
+
+```json
+{
+  "type": "index_parallel",
+  "spec": {
+    "ioConfig": {
+      "type": "index_parallel",
+      "inputSource": {
+        "type": "inline",
+        "data": "{\"time\":\"2024-01-01T00:00:00.000Z\",\"string_example\":\"my_string\",\"number_example\":99}\n{\"time\":\"2024-01-02T00:00:00.000Z\",\"string_example\":\"\",\"number_example\":0}\n{\"time\":\"2024-01-03T00:00:00.000Z\",\"string_example\":null,\"number_example\":null}"
+      },
+      "inputFormat": {
+        "type": "json"
+      }
+    },
+    "tuningConfig": {
+      "type": "index_parallel",
+      "partitionsSpec": {
+        "type": "dynamic"
+      }
+    },
+    "dataSchema": {
+      "dataSource": "inline_data_native",
+      "timestampSpec": {
+        "column": "time",
+        "format": "iso"
+      },
+      "dimensionsSpec": {
+        "dimensions": [
+          "string_example",
+          {
+            "type": "long",
+            "name": "number_example"
+          }
+        ]
+      },
+      "granularitySpec": {
+        "queryGranularity": "none",
+        "rollup": false,
+        "segmentGranularity": "MONTH"
+      },
+      "transformSpec": {
+        "transforms": [
+          {
+            "type": "expression",
+            "name": "string_example",
+            "expression": "COALESCE(\"string_example\",'')"
+          },
+          {
+            "type": "expression",
+            "name": "number_example",
+            "expression": "NVL(\"number_example\",0)"
+          }
+        ]
+      }
+    }
+  }
+}
+```
+
+</TabItem>
+</Tabs>
+
+Druid ingests the data with no null values as follows:
+
+| `__time` | `string_examle` | `number_example`|
+| -- | -- | -- |
+| `2024-01-01T00:00:00.000Z`| `my_string`| 99 |
+| `2024-01-02T00:00:00.000Z`| `empty`| 0 |
+| `2024-01-03T00:00:00.000Z`| `empty`| 0 |
+
+### Coerce empty strings to null at ingestion time
+
+In legacy mode, Druid recognized empty strings as nulls for equality comparison.
+If your queries rely on empty strings to represent nulls, you can coerce empty strings to null at ingestion time using NULLIF.
+
+For example, consider the following sample input data:
+
+```json
+{"time":"2024-01-01T00:00:00.000Z","string_example":"my_string"}
+{"time":"2024-01-02T00:00:00.000Z","string_example":""}
+{"time":"2024-01-03T00:00:00.000Z","string_example":null}
+```
+
+In legacy mode, Druid wrote an empty string for the third record.
+Therefore the following query returned 2:
+
+```sql
+SELECT count(*)
+FROM "null_string"
+WHERE "string_example" IS NULL
+```
+
+In SQL compliant mode, Druid differentiates between empty strings and nulls, so the same query would return 1.
+The following example shows how to coerce empty strings into null to accommodate IS NULL comparisons:
+
+<Tabs>
+
+<TabItem value="0" label="SQL-based batcn">
+
+```sql
+REPLACE INTO "null_string" OVERWRITE ALL
+WITH "ext" AS (
+  SELECT *
+  FROM TABLE(
+    EXTERN(
+      '{"type":"inline","data":"{\"time\":\"2024-01-01T00:00:00.000Z\",\"string_example\":\"my_string\"}\n{\"time\":\"2024-01-02T00:00:00.000Z\",\"string_example\":\"\"}\n{\"time\":\"2024-01-03T00:00:00.000Z\",\"string_example\":null}"}',
+      '{"type":"json"}'
+    )
+  ) EXTEND ("time" VARCHAR, "string_example" VARCHAR)
+)
+SELECT
+  TIME_PARSE("time") AS "__time",
+  NULLIF("string_example",'') AS "string_example"
+FROM "ext"
+PARTITIONED BY MONTH
+```
+
+</TabItem>
+
+<TabItem value="1" label="JSON-based batch">
+
+```json
+{
+  "type": "index_parallel",
+  "spec": {
+    "ioConfig": {
+      "type": "index_parallel",
+      "inputSource": {
+        "type": "inline",
+        "data": "{\"time\":\"2024-01-01T00:00:00.000Z\",\"string_example\":\"my_string\"}\n{\"time\":\"2024-01-02T00:00:00.000Z\",\"string_example\":\"\"}\n{\"time\":\"2024-01-03T00:00:00.000Z\",\"string_example\":null}"
+      },
+      "inputFormat": {
+        "type": "json"
+      }
+    },
+    "tuningConfig": {
+      "type": "index_parallel",
+      "partitionsSpec": {
+        "type": "dynamic"
+      }
+    },
+    "dataSchema": {
+      "dataSource": "null_string",
+      "timestampSpec": {
+        "column": "time",
+        "format": "iso"
+      },
+      "transformSpec": {
+        "transforms": [
+          {
+            "type": "expression",
+            "expression": "case_searched((\"string_example\" == ''),null,\"string_example\")",
+            "name": "string_example"
+          }
+        ]
+      },
+      "dimensionsSpec": {
+        "dimensions": [
+          "string_example"
+        ]
+      },
+      "granularitySpec": {
+        "queryGranularity": "none",
+        "rollup": false,
+        "segmentGranularity": "month"
+      }
+    }
+  }
+}
+```
+
+</TabItem>
+</Tabs>
+
+Druid ingests the data with no empty strings as follows:
+
+| `__time` | `string_examle` |
+| -- | -- | -- |
+| `2024-01-01T00:00:00.000Z`| `my_string`|
+| `2024-01-02T00:00:00.000Z`| `null`|
+| `2024-01-03T00:00:00.000Z`| `null`|
+
+Therefore `SELECT count(*) FROM "null_string" WHERE "string_example" IS NULL` returns 2.
+
+### Rewrite your queries to be SQL compliant
+
+If you want to maintain null values in your data within Druid, you can use the following ANSI SQL compliant querying strategies to achieve the same results as legacy null handling:
+
+- Modify inequality queries to include null values.
+  For example, `x <> 'some value'` becomes `(x <> 'some value' OR x IS NULL)`.
+- Use COALESCE or NVL to replace nulls with a value.
+  For example, `x + 1` becomes `NVL(numeric_value, 0)+1`
+
+Consider the following Druid datasource `null_example`:
+
+| `__time` | `string_examle` | `number_example`|
+| -- | -- | -- |
+| `2024-01-01T00:00:00.000Z`| `my_string`| 99 |
+| `2024-01-02T00:00:00.000Z`| `empty`| 0 |
+| `2024-01-03T00:00:00.000Z`| `null`| null |
+
+Druid excludes null strings from equality comparisons. For example:
+
+```sql
+SELECT COUNT(*) AS count_example
+FROM "null_example"
+WHERE "string_example"<> 'my_string'
+```
+
+Druid returns 1 because null is considered unknown: neither equal nor unequal to the value.
+
+To count null values in the result, you can use an OR operator:
+
+```sql
+SELECT COUNT(*) AS count_example
+FROM "null_example"
+WHERE ("string_example"<> 'my_string') OR "string_example" IS NULL
+```
+
+Druid returns 2.
+To achieve the same result, you can use IS DISTINCT FROM for null-safe comparison:
+
+```sql
+SELECT COUNT(*) as count_example
+FROM "null_example"
+WHERE "string_example" IS DISTINCT FROM 'my_string'
+```
+
+Similarly, arithmetic operators on null return null. For example:
+
+```sql
+SELECT "number_example" + 1 AS additon_example
+FROM "null_example"
+```
+
+Druid returns the following because null + any value is null for the ANSI SQL standard:
+
+| `addition_example`|
+| -- |
+| 100 |
+| 1 |
+| null |
+
+Use NVL to avoid nulls with arithmetic. For example:
+
+```sql
+SELECT NVL("number_example",0) + 1 AS additon_example
+FROM "null_example"
+```
+
+Druid returns the following:
+
+| `addition_example` |
+| -- |
+| 100 |
+| 1 |
+| null |
+
+## Learn more
+
+See the following topics for more information:
+ - [Null handling tutorial](../tutorials/tutorial-sql-null.md) to learn how the default null handling works in Druid.
+ - [Null values](../querying/sql-data-types.md#null-values) for a description of Druid's behavior with null values.
+ - [Handling null values](../design/segments.md#handling-null-values) for details about how Druid stores null values.

docs/release-info/migration-guide.md

@@ -30,12 +30,18 @@ The guides in this section outline breaking changes introduced in Druid 25.0.0 a
 
 ## Migrate from multi-value dimensions to arrays
 
-Druid now supports SQL-compliant array types. Whenever possible, you should use the array type over multi-value dimensions. See [Migration guide: MVDs to arrays](migr-mvd-array.md).
+Druid now supports SQL-compliant array types. Whenever possible, you should use the array type over multi-value dimensions. See [Migration guide: MVDs to arrays](./migr-mvd-array.md).
 
 ## Migrate to front-coded dictionary encoding
 
-Druid encodes string columns into dictionaries for better compression. Front-coded dictionary encoding reduces storage and improves performance by optimizing for strings that share similar beginning substrings. See [Migration guide: front-coded dictionaries](migr-front-coded-dict.md) for more information.
+Druid encodes string columns into dictionaries for better compression. Front-coded dictionary encoding reduces storage and improves performance by optimizing for strings that share similar beginning substrings. See [Migration guide: front-coded dictionaries](./migr-front-coded-dict.md) for more information.
 
 ## Migrate from `maxSubqueryRows` to `maxSubqueryBytes`
 
-Druid allows you to set a byte-based limit on subquery size to prevent Brokers from running out of memory when handling large subqueries. The byte-based subquery limit overrides Druid's row-based subquery limit. We recommend that you move towards using byte-based limits starting in Druid 30.0.0. See [Migration guide: subquery limit](migr-subquery-limit.md) for more information.
+Druid allows you to set a byte-based limit on subquery size to prevent Brokers from running out of memory when handling large subqueries. The byte-based subquery limit overrides Druid's row-based subquery limit. We recommend that you move towards using byte-based limits starting in Druid 30.0.0. See [Migration guide: subquery limit](./migr-subquery-limit.md) for more information.
+
+## Migrate to SQL compliant null handling mode
+
+By default, the Druid [null handling](../querying/sql-data-types.md#null-values) mode is now compliant with ANSI SQL.
+This guide provides strategies for Druid operators and users who rely on the legacy Druid null handling behavior in their applications to transition to ANSI SQL compliant mode.  See [Migration guide: SQL compliant mode](./migr-ansi-sql-null.md
+) for more information.

docs/tutorials/tutorial-sql-null.md

@@ -118,7 +118,7 @@ Druid returns the following:
 |`another_value`|1|1|
 |`some_value`|1|1|
 
-Also note that GROUP BY expressions yields distinct entries for `null` and the empty string.
+Also note that GROUP BY expressions yield distinct entries for `null` and the empty string.
 
 ### Filter for empty strings in addition to null
 

website/sidebars.json

@@ -376,6 +376,7 @@
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         "release-info/migr-front-coded-dict",
         "release-info/migr-subquery-limit",
+        "release-info/migr-ansi-sql-null",
         "release-info/migr-mvd-array"
       ]
     }